A basic problem exists in obtaining accurate statistics of aircraft types using specific runways at airports. Such information is used for optimizing runway utilization and to make projections for future plans. Presently, this type of information is obtained by human observers manually noting the aircraft types using runways for arrivals or departures and to determine the taxi-and-hold times between two given locations for various aircraft types.
There is also need to assist ground controllers at airports when there is heavy traffic or poor visibility in tracking the location of aircraft and in knowing the types of aircraft involved with a minimum of communication between the control tower and aircraft.
At airports with aircraft noise monitoring systems there is a need to automatically classify the type of aircraft causing excessive noise after take-off or before landing. Such aircraft type identification is presently accomplished by observers or by reconstruction using the play-back of recordings of radio communications between the control tower and aircraft.
Automatic aircraft noise monitoring systems also require more reliable means to detect the aircraft traffic pattern at the airport in order to optimize system tracking and noise classification parameters. Presently automatic traffic pattern detection is accomplished by wind velocity sensors and acoustic sensors which are often unreliable in light fluky winds or due to false noise events respectively.